BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911958921379840 |
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| author | Singh, Shubham Bewoor, Mrunal Ranapurwala, Ammar Rai, Satyam Patil, Sheetal |
| author_facet | Singh, Shubham Bewoor, Mrunal Ranapurwala, Ammar Rai, Satyam Patil, Sheetal |
| contents | The work proposes a novel deep-learning framework for the synthesis of three-dimensional MRI volumes from corresponding 3D ultrasound images of the brain, leveraging a modified iteration of the Pix2Pix Generative Adversarial Network (GAN) model. Addressing the formidable challenge of bridging the modality disparity between ultrasound and MRI, this research holds promise for transformative applications in medical diagnostics and treatment planning within the neuroimaging domain. While the findings reveal a discernible degree of similarity between the synthesized MRI volumes and anticipated outcomes, they fall short of practical deployment standards, primarily due to constraints associated with dataset scale and computational resources. The methodology yields MRI volumes with a satisfactory similarity score, establishing a foundational benchmark for subsequent investigations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08608 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI Singh, Shubham Bewoor, Mrunal Ranapurwala, Ammar Rai, Satyam Patil, Sheetal Image and Video Processing Computer Vision and Pattern Recognition The work proposes a novel deep-learning framework for the synthesis of three-dimensional MRI volumes from corresponding 3D ultrasound images of the brain, leveraging a modified iteration of the Pix2Pix Generative Adversarial Network (GAN) model. Addressing the formidable challenge of bridging the modality disparity between ultrasound and MRI, this research holds promise for transformative applications in medical diagnostics and treatment planning within the neuroimaging domain. While the findings reveal a discernible degree of similarity between the synthesized MRI volumes and anticipated outcomes, they fall short of practical deployment standards, primarily due to constraints associated with dataset scale and computational resources. The methodology yields MRI volumes with a satisfactory similarity score, establishing a foundational benchmark for subsequent investigations. |
| title | BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.08608 |